Medicine and Health Sciences Commons^™

Multisite Evaluation Of Prediction Models For Emergency Department Crowding Before And During The Covid-19 Pandemic, Ari J Smith, Brian W Patterson, Michael S Pulia, John Mayer, Rebecca J Schwei, Radha Nagarajan, Frank Liao, Manish N Shah, Justin J Boutilier

Student and Faculty Publications

OBJECTIVE: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.

MATERIALS AND METHODS: We obtained 4 datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-coronavirus disease (COVID) (January 1, 2019-February 1, 2020) and COVID-era (May 15, 2020-February 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than 4 h was above a prescribed historical percentile. We trained a random forest …

Uncovering The Role Of Fat-Infiltrated Axillary Lymph Nodes In Obesity-Related Diseases With Statistical And Machine Learning Analyses, Qingyuan Song

Dartmouth College Ph.D Dissertations

The link between obesity and pathogenesis is a complex and multifaceted area of research that is yet to be fully understood. Ample evidence exists to demonstrate the direct relationship between excessive internal fat and various health conditions such as cancer, and metabolic and cardiovascular diseases. The infiltration of ectopic fat into axillary lymph nodes, observable on breast cancer screening images, has been shown to be correlated with body mass index (BMI) in women undergoing screening. This study aimed to explore the relationship between fat-infiltrated axillary lymph nodes (FIN) and obesity-related diseases, with the goal of evaluating the clinical value of …

Habitat Imaging Biomarkers For Diagnosis And Prognosis In Cancer Patients Infected With Covid-19, Muhammad Aminu, Divya Yadav, Lingzhi Hong, Elliana Young, Paul Edelkamp, Maliazurina Saad, Morteza Salehjahromi, Pingjun Chen, Sheeba J Sujit, Melissa M Chen, Bradley Sabloff, Gregory Gladish, Patricia M De Groot, Myrna C B Godoy, Tina Cascone, Natalie I Vokes, Jianjun Zhang, Kristy K Brock, Naval Daver, Scott E Woodman, Hussein A Tawbi, Ajay Sheshadri, J Jack Lee, David Jaffray, D Code Team, Carol C Wu, Caroline Chung, Jia Wu

Student and Faculty Publications

OBJECTIVES: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population.

METHODS: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and …

A Review Of Risk Concepts And Models For Predicting The Risk Of Primary Stroke, Elizabeth Hunter, John D. Kelleher

Articles

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the …

Effectiveness Of Machine Learning Classifiers For Cataract Screening, Ronald Cheung

Electronic Thesis and Dissertation Repository

Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to …

Incidence Of Cancer, Depression, And Economic Burden Of Prescription Nsaids Among Older Adults With Osteoarthritis: Statistical And Machine Learning Approaches, Nazneen Fatima Mohammed Umer Shaikh

Graduate Theses, Dissertations, and Problem Reports

Understanding the role of chronic inflammation among older adults is critical because of its implications on chronic inflammatory conditions as well as its interaction with anti-inflammatory medications. Osteoarthritis (OA) is a heterogeneous multi-faceted joint disease with multi-tissue involvement of varying severity, and increasing evidence demonstrates a key role of chronic inflammation in its pathogenesis. Approximately 30 million adults in the United States have OA, and it is reported that a large proportion (43%) of them are older than 65 years. OA is typically treated using non-steroidal anti-inflammatory drugs (NSAIDs) for minimizing pain and reducing inflammation. NSAIDs have consistently shown clinically …

Enhancing Drug Overdose Mortality Surveillance Through Natural Language Processing And Machine Learning, Patrick J. Ward

Theses and Dissertations--Epidemiology and Biostatistics

Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized …

The Future Of Zoonotic Risk Prediction, Colin J. Carlson, Maxwell J. Farrell, Zoe Grange, Barbara A. Han, Nardus Mollentze, Alexandra L. Phelan, Angela L. Rasmussen, Gregory F. Albery, Bernard Bett, David Brett-Major, Lily E. Cohen, Tad Dallas, Evan A. Eskew, Anna C. Fagre, Kristian M. Forbes, Rory Gibb, Sam Halabi, Charlotte C. Hammer, Rebecca Katz, Jason Kindrachuk, Renata L. Muylaert, Felicia B. Nutter, Joseph Ogola, Kevin J. Olival, Michelle Rourke, Sadie J. Ryan, Noam Ross, Stephanie N. Seifert, Tarja Sironen, Claire J. Standley, Kishana Taylor, Marietjie Venter, Paul W. Webala

Journal Articles: Epidemiology

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open …

Predictive Modeling Of Influenza In New England Using A Recurrent Deep Neural Network, Alfred Amendolara

Theses

Predicting seasonal variation in influenza epidemics is an ongoing challenge. To better predict seasonal influenza and provide early warning of pandemics, a novel approach to Influenza-Like-Illness (ILI) prediction was developed. This approach combined a deep neural network with ILI, climate, and population data. A predictive model was created using a deep neural network based on TensorFlow 2.0 Beta. The model used Long-Short Term Memory (LSTM) nodes. Data was collected from the Center for Disease Control, the National Center for Environmental Information (NCEI) and the United States Census Bureau. These parameters were temperature, precipitation, wind speed, population size, vaccination rate and …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Clinical Research In Pneumonia: Role Of Artificial Intelligence, Timothy L. Wiemken, Robert R. Kelley, William A. Mattingly, Julio A. Ramirez

The University of Louisville Journal of Respiratory Infections

No abstract provided.

Predictors And Health Outcomes Of Treatment-Resistant Depression Among Adults With Chronic Non-Cancer Pain Conditions And Major Depressive Disorder, Drishti Shah

Graduate Theses, Dissertations, and Problem Reports

Understanding major depressive disorder (MDD) as a comorbidity in patients with chronic non-cancer pain conditions (CNPC) is of importance because of the high prevalence and well documented bi-directional relationship between MDD and pain. Furthermore, presence of CNPC among adults with MDD often reduces benefits of antidepressant therapy, thereby increasing the possibility of treatment resistance. Treatment-resistant depression (TRD) commonly defined as insufficient response to multiple antidepressant trials, often worsens depression and pain symptoms and can amplify the clinical and economic burden among adults with CNPC and MDD. Additionally, long-term opioid therapy (LTOT) may be prescribed at a higher rate to adults …

Predicting 30-Day Mortality In Hospitalized Patients With Community-Acquired Pneumonia Using Statistical And Machine Learning Approaches, Timothy L. Wiemken, Stephen P. Furmanek, William A. Mattingly, Brian E. Guinn, Rodrigo Cavallazzi, Rafael Fernandez-Botran, Leslie A Wolf, Connor L. English, Julio A. Ramirez

The University of Louisville Journal of Respiratory Infections

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP.

Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine …

Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young

Graduate Theses and Dissertations

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is …